Longitudinal epidemiological studies in Alzheimer's disease related dementia have used two-phase sampling designs to estimate population characteristics such as disease incidence and the associations between disease and putative risk factors. Statistical analyses on data from these studies are complicated by the longitudinal design, complex sampling schemes, various attrition patterns, and multiple outcomes of interest. In particular, current statistical methods have all failed to deal with the problem of missing outcomes due to death of elderly subjects, which is likely to be non-ignorable to likelihood based inference. It is well known that analyses ignoring the missing data problem may lead to biased results. The proposed research project is to develop statistical methods for the analysis of longitudinal dementia data in the presence of non-ignorable missing data due to death. Specifically, three methods are proposed. The first is a selection model approach where the missing data due to death mechanism is modeled to depend on disease status prior to death. The second is a mixture model approach where the disease outcome and death process are modeled to share a common set of random effect parameters thought to represent the subjects' strength (or susceptibility) for both disease and death. The third is to model disease incidence and mortality simultaneously using an illness-death stochastic model. The applicants also address some questions for designing longitudinal dementia studies and consider adjustment for misclassification error on risk exposure when estimating association between disease and risk factors. The applicants hope that the proposed research can lead to unified and efficient statistical methods in longitudinal dementia studies and hence reduce the bias, inefficiency, and variation in study results due to statistical analysis approach. The applicant states that the methods developed in this proposal are widely applicable to other epidemiological studies using longitudinal two-phase sampling designs.